Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

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Low-Altitude Unmanned Aerial Vehicle for Real-Time Greenhouse Plant Disease Monitoring Using Convolutional Neural Network

10.1007/978-981-16-2712-5_6 ◽

2021 ◽

pp. 63-76

Author(s):

C. S. Arvind ◽

K. Prajwal ◽

Amrut C. Patil ◽

A. Ashwin Kumar ◽

A. Sreedevi ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Plant Disease ◽

Disease Monitoring ◽

Aerial Vehicle ◽

Greenhouse Plant ◽

Low Altitude

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Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719865882 ◽

2019 ◽

Vol 15 (8) ◽

pp. 155014771986588 ◽

Cited By ~ 3

Author(s):

Shan Meng ◽

Xin Dai ◽

Bicheng Xiao ◽

Yimin Zhou ◽

Yumei Li ◽

...

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicle ◽

Millimeter Wave ◽

Estimation Method ◽

Attitude Estimation ◽

Base Stations ◽

Communication Link ◽

Multiple Sensors ◽

Channel Tracking ◽

Aerial Vehicle

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

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Design of facial recognition system implemented in an unmanned aerial vehicle for citizen security in Latin America

ITM Web of Conferences ◽

10.1051/itmconf/20192704002 ◽

2019 ◽

Vol 27 ◽

pp. 04002

Author(s):

Diego Herrera ◽

Hiroki Imamura

Keyword(s):

Neural Network ◽

Latin America ◽

Deep Learning ◽

Mobile Devices ◽

Unmanned Aerial Vehicle ◽

Facial Recognition ◽

Recognition System ◽

Criminal Records ◽

Aerial Vehicle ◽

Facial Recognition System

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.

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Coarse-to-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency

Neurocomputing ◽

10.1016/j.neucom.2019.03.102 ◽

2020 ◽

Vol 398 ◽

pp. 555-565 ◽

Cited By ~ 1

Author(s):

Jing Zhang ◽

Xi Liang ◽

Meng Wang ◽

Liheng Yang ◽

Li Zhuo

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Unmanned Aerial Vehicle ◽

Aerial Vehicle ◽

Motion Saliency ◽

Coarse To Fine

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Autopilot design for tilt-rotor unmanned aerial vehicle with nacelle mounted wing extension using single hidden layer perceptron neural network

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016664926 ◽

2016 ◽

Vol 231 (11) ◽

pp. 1979-1992 ◽

Cited By ~ 1

Author(s):

Youngshin Kang ◽

Nakwan Kim ◽

Byoung-Soo Kim ◽

Min-Jea Tahk

Keyword(s):

Neural Network ◽

Unmanned Aerial Vehicle ◽

Lyapunov Equation ◽

Pole Placement ◽

Dynamic Inversion ◽

North West ◽

Tilt Rotor ◽

Neural Network Controller ◽

Aerial Vehicle ◽

Hidden Layer

Single hidden layer perceptron neural network controllers combined with dynamic inversion are applied to the tilt-rotor unmanned aerial vehicle and its variant model with the nacelle mounted wing extension. The bandwidths of the inner loop and outer loop of the controller are designed using the timescale separation approach, which uses the combined analysis of the two loops. The bandwidth of each loop is selected to be close to each other using a combination of the pseudo-control-hedging and the pole-placement method. Similar to the previous studies on sigma-pi neural network, the dynamic inversion at hover conditions of the original tilt-rotor model is used as a baseline for both aircraft, and the compatible solution to the Lyapunov equation is suggested. The single hidden layer perceptron neural network minimizes the error of the inversion model through the back-propagation adaptation. The waypoint guidance is applied to the outermost loop of the neural network controller for autonomous flight which includes vertical take-off and landing as well as nacelle conversion. The simulation results under the two wind conditions for the tilt-rotor aircraft and its variant are presented. The south and north-west wind directions are simulated in order to compare with the results from the existing sigma-pi neural network, and the estimation results of the wind are presented.

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